The increasing importance of renewable energies in power grids places new demands on forecasting required and available feed-in powers in order thus to be able to control the grid in the best possible manner. This challenge is reflected in the concepts of so-called “smart grids”, which also permit decentralized control of the energy flows.
A so-called error correction neural network (ECNN) is disclosed in [Zimmermann H. G., Neuneier R., Grothmann R.: Modeling of Dynamical Systems by Error Correction Neural Networks in Modeling and Forecasting Financial Data, Techniques of Nonlinear Dynamics, Eds. Soofi, A. and Cao, L. Kluwer Academic Publishers, 2002, pp. 237-263].
In order to be able to compensate for a control of load flows in a subordinate grid section of the power grid without in the process having to make use of power from a superordinate grid section, it is necessary to know the expected feed-in powers in advance. Thus, the power balance and/or the operating means load can be compensated for by controllable loads, energy stores or energy producers (e.g. combined heat and power installations (CHP installations)), but this requires a certain amount of lead-in time in order to be able to take into account the dynamics (temporally delayed response behavior) thereof and ultimately to ensure that the controllable loads, energy stores or energy producers are ready for operation in good time.
This is problematic, particularly in the aforementioned renewable energies, because the power provided can vary strongly in the short term.